1
|
Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
Collapse
Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
| |
Collapse
|
2
|
Raffo-Romero A, Ziane-Chaouche L, Salomé-Desnoulez S, Hajjaji N, Fournier I, Salzet M, Duhamel M. A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses. CELL REPORTS METHODS 2024; 4:100792. [PMID: 38861990 PMCID: PMC11228374 DOI: 10.1016/j.crmeth.2024.100792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 06/13/2024]
Abstract
3D tumoroids have revolutionized in vitro/ex vivo cancer biology by recapitulating the complex diversity of tumors. While tumoroids provide new insights into cancer development and treatment response, several limitations remain. As the tumor microenvironment, especially the immune system, strongly influences tumor development, the absence of immune cells in tumoroids may lead to inappropriate conclusions. Macrophages, key players in tumor progression, are particularly challenging to integrate into the tumoroids. In this study, we established three optimized and standardized methods for co-culturing human macrophages with breast cancer tumoroids: a semi-liquid model and two matrix-embedded models tailored for specific applications. We then tracked interactions and macrophage infiltration in these systems using flow cytometry and light sheet microscopy and showed that macrophages influenced not only tumoroid molecular profiles but also chemotherapy response. This underscores the importance of increasing the complexity of 3D models to more accurately reflect in vivo conditions.
Collapse
Affiliation(s)
- Antonella Raffo-Romero
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France
| | - Lydia Ziane-Chaouche
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France
| | - Sophie Salomé-Desnoulez
- University Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
| | - Nawale Hajjaji
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France; Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Isabelle Fournier
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France
| | - Michel Salzet
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France.
| | - Marie Duhamel
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Equipe Labellisée Ligue Contre le Cancer, Lille, France.
| |
Collapse
|
3
|
Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
Collapse
Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| |
Collapse
|
4
|
Tian H, Rajbhandari P, Tarolli J, Decker AM, Neelakantan TV, Angerer T, Zandkarimi F, Remotti H, Frache G, Winograd N, Stockwell BR. Multimodal mass spectrometry imaging identifies cell-type-specific metabolic and lipidomic variation in the mammalian liver. Dev Cell 2024; 59:869-881.e6. [PMID: 38359832 DOI: 10.1016/j.devcel.2024.01.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 05/11/2023] [Accepted: 01/26/2024] [Indexed: 02/17/2024]
Abstract
Spatial single-cell omics provides a readout of biochemical processes. It is challenging to capture the transient lipidome/metabolome from cells in a native tissue environment. We employed water gas cluster ion beam secondary ion mass spectrometry imaging ([H2O]n>28K-GCIB-SIMS) at ≤3 μm resolution using a cryogenic imaging workflow. This allowed multiple biomolecular imaging modes on the near-native-state liver at single-cell resolution. Our workflow utilizes desorption electrospray ionization (DESI) to build a reference map of metabolic heterogeneity and zonation across liver functional units at tissue level. Cryogenic dual-SIMS integrated metabolomics, lipidomics, and proteomics in the same liver lobules at single-cell level, characterizing the cellular landscape and metabolic states in different cell types. Lipids and metabolites classified liver metabolic zones, cell types and subtypes, highlighting the power of spatial multi-omics at high spatial resolution for understanding celluar and biomolecular organizations in the mammalian liver.
Collapse
Affiliation(s)
- Hua Tian
- Environmental and Occupational Health, Pitt Public Health, Pittsburgh, PA 15261, USA; Children's Neuroscience Institute, School of Medicine, Pittsburgh, PA 15224, USA.
| | - Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | | | - Aubrianna M Decker
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | | | - Tina Angerer
- The Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg; Department of Pharmaceutical Biosciences, Uppsala University, 751 05 Uppsala, Sweden
| | | | - Helen Remotti
- Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Gilles Frache
- The Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg
| | - Nicholas Winograd
- Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; Department of Chemistry, Columbia University, New York, NY 10027, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA.
| |
Collapse
|
5
|
Kumar BS. Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview. Mass Spectrom (Tokyo) 2024; 13:A0142. [PMID: 38435075 PMCID: PMC10904931 DOI: 10.5702/massspectrometry.a0142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/06/2024] [Indexed: 03/05/2024] Open
Abstract
Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.
Collapse
|
6
|
Bag S, Oetjen J, Shaikh S, Chaudhary A, Arun P, Mukherjee G. Impact of spatial metabolomics on immune-microenvironment in oral cancer prognosis: a clinical report. Mol Cell Biochem 2024; 479:41-49. [PMID: 36966422 DOI: 10.1007/s11010-023-04713-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 03/15/2023] [Indexed: 03/27/2023]
Abstract
MALDI imaging for metabolites and immunohistochemistry for 38 immune markers was used to characterize the spatial biology of 2 primary oral tumours, one from a patient with an early recurrence (Tumour R), and the other from a patient with no recurrence 2 years after treatment completion (Tumour NR). Tumour R had an increased purine nucleotide metabolism in different regions of tumour and adenosine-mediated suppression of immune cells compared to Tumour NR. The differentially expressed markers in the different spatial locations in tumour R were CD33, CD163, TGF-β, COX2, PD-L1, CD8 and CD20. These results suggest that altered tumour metabolomics concomitant with a modified immune microenvironment could be a potential marker of recurrence.
Collapse
Affiliation(s)
- Swarnendu Bag
- Tata Medical Center, Newtown, Kolkata, 700 160, India
- CSIR-Institute of Genomics and Integrative Biology (IGIB), Mall Road, New Delhi, 110 007, India
| | | | - Soni Shaikh
- Tata Medical Center, Newtown, Kolkata, 700 160, India
| | | | | | | |
Collapse
|
7
|
Hochmann S, Ou K, Poupardin R, Mittermeir M, Textor M, Ali S, Wolf M, Ellinghaus A, Jacobi D, Elmiger JAJ, Donsante S, Riminucci M, Schäfer R, Kornak U, Klein O, Schallmoser K, Schmidt-Bleek K, Duda GN, Polansky JK, Geissler S, Strunk D. The enhancer landscape predetermines the skeletal regeneration capacity of stromal cells. Sci Transl Med 2023; 15:eabm7477. [PMID: 36947595 DOI: 10.1126/scitranslmed.abm7477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Multipotent stromal cells are considered attractive sources for cell therapy and tissue engineering. Despite numerous experimental and clinical studies, broad application of stromal cell therapeutics is not yet emerging. A major challenge is the functional diversity of available cell sources. Here, we investigated the regenerative potential of clinically relevant human stromal cells from bone marrow (BMSCs), white adipose tissue, and umbilical cord compared with mature chondrocytes and skin fibroblasts in vitro and in vivo. Although all stromal cell types could express transcription factors related to endochondral ossification, only BMSCs formed cartilage discs in vitro that fully regenerated critical-size femoral defects after transplantation into mice. We identified cell type-specific epigenetic landscapes as the underlying molecular mechanism controlling transcriptional stromal differentiation networks. Binding sites of commonly expressed transcription factors in the enhancer and promoter regions of ossification-related genes, including Runt and bZIP families, were accessible only in BMSCs but not in extraskeletal stromal cells. This suggests an epigenetically predetermined differentiation potential depending on cell origin that allows common transcription factors to trigger distinct organ-specific transcriptional programs, facilitating forward selection of regeneration-competent cell sources. Last, we demonstrate that viable human BMSCs initiated defect healing through the secretion of osteopontin and contributed to transient mineralized bone hard callus formation after transplantation into immunodeficient mice, which was eventually replaced by murine recipient bone during final tissue remodeling.
Collapse
Affiliation(s)
- Sarah Hochmann
- Cell Therapy Institute, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University (PMU), 5020 Salzburg, Austria
| | - Kristy Ou
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), T Cell Epigenetics, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Rodolphe Poupardin
- Cell Therapy Institute, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University (PMU), 5020 Salzburg, Austria
| | - Michaela Mittermeir
- Cell Therapy Institute, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University (PMU), 5020 Salzburg, Austria
| | - Martin Textor
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Salaheddine Ali
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, 13353 Berlin, Germany
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin Wolf
- Cell Therapy Institute, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University (PMU), 5020 Salzburg, Austria
| | - Agnes Ellinghaus
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Dorit Jacobi
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Juri A J Elmiger
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Samantha Donsante
- Department of Molecular Medicine, Sapienza University of Rome, 00189 Rome, Italy
| | - Mara Riminucci
- Department of Molecular Medicine, Sapienza University of Rome, 00189 Rome, Italy
| | - Richard Schäfer
- Institute for Transfusion Medicine and Immunohematology, Goethe University Hospital, German Red Cross Blood Service Baden-Württemberg-Hessen gGmbH, 60323 Frankfurt am Main, Germany
- Institute for Transfusion Medicine and Gene Therapy, Medical Center - University of Freiburg, 79106 Freiburg, Germany
| | - Uwe Kornak
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Institute for Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, 13353 Berlin, Germany
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
- Institute of Human Genetics, University Medical Center Göttingen, 37073 Göttingen, Germany
| | - Oliver Klein
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
| | | | - Katharina Schmidt-Bleek
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Georg N Duda
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Julia K Polansky
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), T Cell Epigenetics, Augustenburger Platz 1, 13353 Berlin, Germany
- German Rheumatism Research Centre (DRFZ), 10117 Berlin, Germany
| | - Sven Geissler
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute (JWI), Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Center for Advanced Therapies (BECAT), Charité - Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Dirk Strunk
- Cell Therapy Institute, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University (PMU), 5020 Salzburg, Austria
| |
Collapse
|
8
|
McDowell CT, Lu X, Mehta AS, Angel PM, Drake RR. Applications and continued evolution of glycan imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:674-705. [PMID: 34392557 PMCID: PMC8946722 DOI: 10.1002/mas.21725] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/16/2021] [Accepted: 08/03/2021] [Indexed: 05/03/2023]
Abstract
Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.
Collapse
Affiliation(s)
- Colin T. McDowell
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Xiaowei Lu
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Anand S. Mehta
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Peggi M. Angel
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Richard R. Drake
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, 29425, USA
| |
Collapse
|
9
|
Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
Collapse
Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
| |
Collapse
|
10
|
Guo A, Chen Z, Li F, Luo Q. Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation. Gigascience 2022; 12:giad021. [PMID: 37039115 PMCID: PMC10087011 DOI: 10.1093/gigascience/giad021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/17/2023] [Accepted: 03/13/2023] [Indexed: 04/12/2023] Open
Abstract
Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
Collapse
Affiliation(s)
- Ang Guo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhiyu Chen
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fang Li
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qian Luo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
11
|
Mrukwa G, Polanska J. DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data. BMC Bioinformatics 2022; 23:538. [PMID: 36503372 PMCID: PMC9743550 DOI: 10.1186/s12859-022-05093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. RESULTS We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). CONCLUSIONS DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .
Collapse
Affiliation(s)
- Grzegorz Mrukwa
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland ,Netguru, Małe Garbary 9, 61-756 Poznań, Poland
| | - Joanna Polanska
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
| |
Collapse
|
12
|
Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
Collapse
Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| |
Collapse
|
13
|
Duhamel M, Drelich L, Wisztorski M, Aboulouard S, Gimeno JP, Ogrinc N, Devos P, Cardon T, Weller M, Escande F, Zairi F, Maurage CA, Le Rhun É, Fournier I, Salzet M. Spatial analysis of the glioblastoma proteome reveals specific molecular signatures and markers of survival. Nat Commun 2022; 13:6665. [PMID: 36333286 PMCID: PMC9636229 DOI: 10.1038/s41467-022-34208-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Molecular heterogeneity is a key feature of glioblastoma that impedes patient stratification and leads to large discrepancies in mean patient survival. Here, we analyze a cohort of 96 glioblastoma patients with survival ranging from a few months to over 4 years. 46 tumors are analyzed by mass spectrometry-based spatially-resolved proteomics guided by mass spectrometry imaging. Integration of protein expression and clinical information highlights three molecular groups associated with immune, neurogenesis, and tumorigenesis signatures with high intra-tumoral heterogeneity. Furthermore, a set of proteins originating from reference and alternative ORFs is found to be statistically significant based on patient survival times. Among these proteins, a 5-protein signature is associated with survival. The expression of these 5 proteins is validated by immunofluorescence on an additional cohort of 50 patients. Overall, our work characterizes distinct molecular regions within glioblastoma tissues based on protein expression, which may help guide glioblastoma prognosis and improve current glioblastoma classification.
Collapse
Affiliation(s)
- Marie Duhamel
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France.
| | - Lauranne Drelich
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Maxence Wisztorski
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Soulaimane Aboulouard
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Jean-Pascal Gimeno
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Nina Ogrinc
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Patrick Devos
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000, Lille, France
| | - Tristan Cardon
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France
| | - Michael Weller
- Department of Neurology & Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Fabienne Escande
- CHU Lille, Service de biochimie et biologie moléculaire, CHU Lille, F-59000, Lille, France
| | - Fahed Zairi
- CHU Lille, Service de neurochirurgie, F-59000, Lille, France
| | - Claude-Alain Maurage
- CHU Lille, Service de biochimie et biologie moléculaire, CHU Lille, F-59000, Lille, France
| | - Émilie Le Rhun
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France.
- Department of Neurology & Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
- CHU Lille, Service de biochimie et biologie moléculaire, CHU Lille, F-59000, Lille, France.
| | - Isabelle Fournier
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France.
- Institut Universitaire de France (IUF), 75000, Paris, France.
| | - Michel Salzet
- Univ.Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000, Lille, France.
- Institut Universitaire de France (IUF), 75000, Paris, France.
| |
Collapse
|
14
|
MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues. Metabolites 2022; 12:metabo12060530. [PMID: 35736462 PMCID: PMC9228942 DOI: 10.3390/metabo12060530] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 12/14/2022] Open
Abstract
Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies.
Collapse
|
15
|
Gardner W, Winkler DA, Cutts SM, Torney SA, Pietersz GA, Muir BW, Pigram PJ. Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder. Anal Chem 2022; 94:7804-7813. [PMID: 35616489 DOI: 10.1021/acs.analchem.1c05453] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.
Collapse
Affiliation(s)
- Wil Gardner
- Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia.,La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.,CSIRO Manufacturing, Clayton, Victoria 3168, Australia
| | - David A Winkler
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.,Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, U.K
| | - Suzanne M Cutts
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Steven A Torney
- La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Geoffrey A Pietersz
- Immune Therapies Laboratory, Burnet Institute, Melbourne, Victoria 3004, Australia.,Atherothrombosis and Vascular Biology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | | | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia
| |
Collapse
|
16
|
Dannhorn A, Kazanc E, Hamm G, Swales JG, Strittmatter N, Maglennon G, Goodwin RJA, Takats Z. Correlating Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry for Tissue-Based Pharmacokinetic Studies. Metabolites 2022; 12:metabo12030261. [PMID: 35323705 PMCID: PMC8954739 DOI: 10.3390/metabo12030261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 01/12/2023] Open
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistribution within the tissue is impossible. In the study presented here we used a sample embedding protocol optimized for mass spectrometry imaging (MSI) to prepare up to 15 rat intestine specimens at once. Desorption electrospray ionization (DESI) and matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) were employed to determine the distributions and relative abundances of four benchmarking compounds in the intestinal segments. High resolution MALDI-MSI experiments performed at 10 µm spatial resolution allowed to determine the drug distribution in the different intestinal histological compartments to determine the absorbed and tissue bound fractions of the drugs. The low tissue bound drug fractions, which were determined to account for 56–66% of the total drug, highlight the importance to understand the spatial distribution of drugs within the histological compartments of a given tissue to rationalize concentration differences found in PK studies. The mean drug abundances of four benchmark compounds determined by MSI were correlated with the absolute drug concentrations. Linear regression resulted in coefficients of determination (R2) ranging from 0.532 to 0.926 for MALDI-MSI and R2 values ranging from 0.585 to 0.945 for DESI-MSI, validating a quantitative relation of the imaging data. The good correlation of the absolute tissue concentrations of the benchmark compounds and the MSI data provides a bases for relative quantification of compounds within and between tissues, without normalization to an isotopically labelled standard, provided that the compared tissues have inherently similar ion suppression effects.
Collapse
Affiliation(s)
- Andreas Dannhorn
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Emine Kazanc
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
| | - Gregory Hamm
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - John G. Swales
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Nicole Strittmatter
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Gareth Maglennon
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK;
| | - Richard J. A. Goodwin
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
- Laboratoire PRISM, Inserm U1192, University of Lille, Villeneuve d’Ascq, 59655 Lille, France
- The Rosalind Franklin Institute, Harwell OX11 0QG, UK
- Correspondence:
| |
Collapse
|
17
|
Ogrinc N, Attencourt C, Colin E, Boudahi A, Tebbakha R, Salzet M, Testelin S, Dakpé S, Fournier I. Mass Spectrometry-Based Differentiation of Oral Tongue Squamous Cell Carcinoma and Nontumor Regions With the SpiderMass Technology. FRONTIERS IN ORAL HEALTH 2022; 3:827360. [PMID: 35309279 PMCID: PMC8929397 DOI: 10.3389/froh.2022.827360] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/26/2022] [Indexed: 12/14/2022] Open
Abstract
Oral cavity cancers are the 15th most common cancer with more than 350,000 new cases and ~178,000 deaths each year. Among them, squamous cell carcinoma (SCC) accounts for more than 90% of tumors located in the oral cavity and on oropharynx. For the oral cavity SCC, the surgical resection remains the primary course of treatment. Generally, surgical margins are defined intraoperatively using visual and tactile elements. However, in 15-30% of cases, positive margins are found after histopathological examination several days postsurgery. Technologies based on mass spectrometry (MS) were recently developed to help guide surgical resection. The SpiderMass technology is designed for in-vivo real-time analysis under minimally invasive conditions. This instrument achieves tissue microsampling and real-time molecular analysis with the combination of a laser microprobe and a mass spectrometer. It ultimately acts as a tool to support histopathological decision-making and diagnosis. This pilot study included 14 patients treated for tongue SCC (T1 to T4) with the surgical resection as the first line of treatment. Samples were first analyzed by a pathologist to macroscopically delineate the tumor, dysplasia, and peritumoral areas. The retrospective and prospective samples were sectioned into three consecutive sections and thaw-mounted on slides for H&E staining (7 μm), SpiderMass analysis (20 μm), and matrix-assisted laser desorption ionization (MALDI) MS imaging (12 μm). The SpiderMass microprobe collected lipidometabolic profiles of the dysplasia, tumor, and peritumoral regions annotated by the pathologist. The MS spectra were then subjected to the multivariate statistical analysis. The preliminary data demonstrate that the lipidometabolic molecular profiles collected with the SpiderMass are significantly different between the tumor and peritumoral regions enabling molecular classification to be established by linear discriminant analysis (LDA). MALDI images of the different samples were submitted to segmentation for cross instrument validation and revealed additional molecular discrimination within the tumor and nontumor regions. These very promising preliminary results show the applicability of the SpiderMass to SCC of the tongue and demonstrate its interest in the surgical treatment of head and neck cancers.
Collapse
Affiliation(s)
- Nina Ogrinc
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse – PRISM, Lille, France
| | - Christophe Attencourt
- Department of Pathology, CHU Amiens-Picardie, Amiens, France
- UR7516 CHIMERE, Université de Picardie Jules Verne, Amiens, France
| | - Emilien Colin
- UR7516 CHIMERE, Université de Picardie Jules Verne, Amiens, France
- Department of Maxillofacial Surgery, CHU Amiens-Picardie, Amiens, France
- Institut Faire Faces, Amiens, France
| | - Ahmed Boudahi
- Department of Pathology, CHU Amiens-Picardie, Amiens, France
| | - Riad Tebbakha
- Tumorothèque de Picardie, CHU Amiens-Picardie, Amiens, France
| | - Michel Salzet
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse – PRISM, Lille, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sylvie Testelin
- UR7516 CHIMERE, Université de Picardie Jules Verne, Amiens, France
- Department of Maxillofacial Surgery, CHU Amiens-Picardie, Amiens, France
- Institut Faire Faces, Amiens, France
| | - Stéphanie Dakpé
- UR7516 CHIMERE, Université de Picardie Jules Verne, Amiens, France
- Department of Maxillofacial Surgery, CHU Amiens-Picardie, Amiens, France
- Institut Faire Faces, Amiens, France
| | - Isabelle Fournier
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse – PRISM, Lille, France
- Institut Universitaire de France (IUF), Paris, France
| |
Collapse
|
18
|
Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems. Biointerphases 2022; 17:020802. [DOI: 10.1116/6.0001590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.
Collapse
|
19
|
Tyler BJ, Kassenböhmer R, Peterson RE, Nguyen DT, Freitag M, Glorius F, Ravoo BJ, Arlinghaus HF. Denoising of Mass Spectrometry Images via Inverse Maximum Signal Factors Analysis. Anal Chem 2022; 94:2835-2843. [PMID: 35107995 DOI: 10.1021/acs.analchem.1c04564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Improving signal-to-noise and, thereby, image contrast is one of the key challenges needed to expand the useful applications of mass spectrometry imaging (MSI). Both instrumental and data analysis approaches are of importance. Univariate denoising techniques have been used to improve contrast in MSI images with varying levels of success. Additionally, various multivariate analysis (MVA) methods have proven to be effective for improving image contrast. However, the distribution of important but low intensity ions can be obscured in the MVA analysis, leading to a loss of chemically specific information. In this work we propose inverse maximum signal factors (MSF) denoising as an alternative approach to both denoising and multivariate analysis for MSI imaging. This approach differs from the standard MVA techniques in that the output is denoised images for each original mass peak rather than the frequently difficult to interpret scores and loadings. Five tests have been developed to optimize and validate the resulting denoised images. The algorithm has been tested on a range of simulated data with different levels of noise, correlated noise, varying numbers of underlying components, and nonlinear effects. In the simulations, an excellent correlation between the true images and the denoised images was observed for peaks with an original signal-to-noise ratio as low as 0.1, as long as there was sufficient intensity in the sum of the selected peaks. The power of the approach was then demonstrated on two time-of-flight secondary ion mass spectrometry (ToF-SIMS) images that contained largely uncorrelated noise and a laser post-ionization matrix-assisted laser desorption/ionization mass spectrometry (MALDI-2-MS) image that contained strongly correlated noise. The improvements in signal-to-noise increased with decreasing intensity of the original peaks. A signal-to-noise improvement of as much as two orders of magnitude was achieved for very low intensity peaks. MSF denoising is a powerful addition to the suite of image processing techniques available for studying mass spectrometry images.
Collapse
Affiliation(s)
- Bonnie J Tyler
- Physikalisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| | - Rainer Kassenböhmer
- Physikalisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| | - Richard E Peterson
- Physikalisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| | - D Thao Nguyen
- Organisch-Chemisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Corrensstraße 40, 48149 Münster, Germany
| | - Matthias Freitag
- Organisch-Chemisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Corrensstraße 40, 48149 Münster, Germany
| | - Frank Glorius
- Organisch-Chemisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Corrensstraße 40, 48149 Münster, Germany
| | - Bart Jan Ravoo
- Organisch-Chemisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Corrensstraße 40, 48149 Münster, Germany
| | - Heinrich F Arlinghaus
- Physikalisches Institut and Center for Soft Nanoscience (SoN), Westfälische Wilhelms-Universität Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| |
Collapse
|
20
|
Hajjaji N, Aboulouard S, Cardon T, Bertin D, Robin YM, Fournier I, Salzet M. Path to Clonal Theranostics in Luminal Breast Cancers. Front Oncol 2022; 11:802177. [PMID: 35096604 PMCID: PMC8793283 DOI: 10.3389/fonc.2021.802177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/06/2021] [Indexed: 12/18/2022] Open
Abstract
Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.
Collapse
Affiliation(s)
- Nawale Hajjaji
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Soulaimane Aboulouard
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Tristan Cardon
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Delphine Bertin
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Yves-Marie Robin
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Isabelle Fournier
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut universitaire de France, Paris, France
| | - Michel Salzet
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut universitaire de France, Paris, France
| |
Collapse
|
21
|
Abdelmoula WM, Stopka SA, Randall EC, Regan M, Agar JN, Sarkaria JN, Wells WM, Kapur T, Agar NYR. massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation. Bioinformatics 2022; 38:2015-2021. [PMID: 35040929 PMCID: PMC8963284 DOI: 10.1093/bioinformatics/btac032] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/04/2022] [Accepted: 01/13/2022] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Invicro LLC, Boston, MA 02210, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Michael Regan
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02111, USA
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55902, USA
| | - William M Wells
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA,To whom correspondence should be addressed.
| |
Collapse
|
22
|
Tian X, Zou Z, Yang Z. Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis. Methods Mol Biol 2022; 2437:253-272. [PMID: 34902154 DOI: 10.1007/978-1-0716-2030-4_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.
Collapse
Affiliation(s)
- Xiang Tian
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA
- Dynamic Omics, Center of Genomics Research (CGR), R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Zhu Zou
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA.
| |
Collapse
|
23
|
Hamm G, Maglennon G, Williamson B, Macdonald R, Doherty A, Jones S, Harris J, Blades J, Harmer AR, Barton P, Rawlins PB, Smith P, Winter-Holt J, McMurray L, Johansson J, Fitzpatrick P, McCoull W, Coen M. Pharmacological inhibition of MERTK induces in vivo retinal degeneration: a multimodal imaging ocular safety assessment. Arch Toxicol 2022; 96:613-624. [PMID: 34973110 PMCID: PMC8837544 DOI: 10.1007/s00204-021-03197-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 11/11/2021] [Indexed: 12/26/2022]
Abstract
The receptor tyrosine kinase, MERTK, plays an essential role in homeostasis of the retina via efferocytosis of shed outer nuclear segments of photoreceptors. The Royal College of Surgeons rat model of retinal degeneration has been linked to loss-of-function of MERTK, and together with the MERTK knock-out mouse, phenocopy retinitis pigmentosa in humans with MERTK mutations. Given recent efforts and interest in MERTK as a potential immuno-oncology target, development of a strategy to assess ocular safety at an early pre-clinical stage is critical. We have applied a state-of-the-art, multi-modal imaging platform to assess the in vivo effects of pharmacological inhibition of MERTK in mice. This involved the application of mass spectrometry imaging (MSI) to characterize the ocular spatial distribution of our highly selective MERTK inhibitor; AZ14145845, together with histopathology and transmission electron microscopy to characterize pathological and ultra-structural change in response to MERTK inhibition. In addition, we assessed the utility of a human retinal in vitro cell model to identify perturbation of phagocytosis post MERTK inhibition. We identified high localized total compound concentrations in the retinal pigment epithelium (RPE) and retinal lesions following 28 days of treatment with AZ14145845. These lesions were present in 4 of 8 treated animals, and were characterized by a thinning of the outer nuclear layer, loss of photoreceptors (PR) and accumulation of photoreceptor outer segments at the interface of the RPE and PRs. Furthermore, the lesions were very similar to that shown in the RCS rat and MERTK knock-out mouse, suggesting a MERTK-induced mechanism of PR cell death. This was further supported by the observation of reduced phagocytosis in the human retinal cell model following treatment with AZ14145845. Our study provides a viable, translational strategy to investigate the pre-clinical toxicity of MERTK inhibitors but is equally transferrable to novel chemotypes.
Collapse
Affiliation(s)
- Gregory Hamm
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Gareth Maglennon
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | | | - Ruth Macdonald
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ann Doherty
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Stewart Jones
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Jayne Harris
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - James Blades
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Alexander R Harmer
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | | | | | - Paul Smith
- Oncology R&D, AstraZeneca, Cambridge, UK
| | | | | | - Julia Johansson
- Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Paul Fitzpatrick
- Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | | | - Muireann Coen
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
| |
Collapse
|
24
|
Castellanos-Garcia LJ, Sikora KN, Doungchawee J, Vachet RW. LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects. Analyst 2021; 146:7720-7729. [PMID: 34821231 DOI: 10.1039/d1an01783g] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within ±50 μm. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.
Collapse
Affiliation(s)
| | - Kristen N Sikora
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
| | - Jeerapat Doungchawee
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
| | - Richard W Vachet
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA.
| |
Collapse
|
25
|
Pathmasiri KC, Nguyen TTA, Khamidova N, Cologna SM. Mass spectrometry-based lipid analysis and imaging. CURRENT TOPICS IN MEMBRANES 2021; 88:315-357. [PMID: 34862030 DOI: 10.1016/bs.ctm.2021.10.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for in situ mapping of analytes across a sample. With growing interest in lipid biochemistry, the ability to perform such mapping without antibodies has opened many opportunities for MSI and lipid analysis. Herein, we discuss the basics of MSI with particular emphasis on MALDI mass spectrometry and lipid analysis. A discussion of critical advancements as well as protocol details are provided to the reader. In addition, strategies for improving the detection of lipids, as well as applications in biomedical research, are presented.
Collapse
Affiliation(s)
- Koralege C Pathmasiri
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Thu T A Nguyen
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Nigina Khamidova
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, United States; Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, United States.
| |
Collapse
|
26
|
Loch FN, Klein O, Beyer K, Klauschen F, Schineis C, Lauscher JC, Margonis GA, Degro CE, Rayya W, Kamphues C. Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging. BIOLOGY 2021; 10:1033. [PMID: 34681132 PMCID: PMC8533220 DOI: 10.3390/biology10101033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022]
Abstract
Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment.
Collapse
Affiliation(s)
- Florian N. Loch
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Oliver Klein
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Center for Regenerative Therapies BCRT, Charitéplatz 1, 10117 Berlin, Germany;
| | - Katharina Beyer
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Frederick Klauschen
- Institute for Pathology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany;
- Institute for Pathology, Ludwig-Maximilians-Universität München, 80337 München, Germany
| | - Christian Schineis
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Johannes C. Lauscher
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Georgios A. Margonis
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Claudius E. Degro
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Wael Rayya
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| | - Carsten Kamphues
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (K.B.); (C.S.); (J.C.L.); (C.E.D.); (W.R.); (C.K.)
| |
Collapse
|
27
|
Dieckmann S, Maurer S, Kleigrewe K, Klingenspor M. Spatial Recruitment of Cardiolipins in Inguinal White Adipose Tissue after Cold Stimulation is Independent of UCP1. EUR J LIPID SCI TECH 2021. [DOI: 10.1002/ejlt.202100090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sebastian Dieckmann
- Chair for Molecular Nutritional Medicine TUM School of Life Sciences Technical University of Munich Freising 85354 Germany
- EKFZ – Else Kröner‐Fresenius Center for Nutritional Medicine Technical University of Munich Freising 85354 Germany
- ZIEL – Institute for Food & Health Technical University of Munich Freising 85354 Germany
| | - Stefanie Maurer
- Chair for Molecular Nutritional Medicine TUM School of Life Sciences Technical University of Munich Freising 85354 Germany
- EKFZ – Else Kröner‐Fresenius Center for Nutritional Medicine Technical University of Munich Freising 85354 Germany
- ZIEL – Institute for Food & Health Technical University of Munich Freising 85354 Germany
| | - Karin Kleigrewe
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) Technical University of Munich Freising 85354 Germany
| | - Martin Klingenspor
- Chair for Molecular Nutritional Medicine TUM School of Life Sciences Technical University of Munich Freising 85354 Germany
- EKFZ – Else Kröner‐Fresenius Center for Nutritional Medicine Technical University of Munich Freising 85354 Germany
- ZIEL – Institute for Food & Health Technical University of Munich Freising 85354 Germany
| |
Collapse
|
28
|
Peak learning of mass spectrometry imaging data using artificial neural networks. Nat Commun 2021; 12:5544. [PMID: 34545087 PMCID: PMC8452737 DOI: 10.1038/s41467-021-25744-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 08/18/2021] [Indexed: 02/07/2023] Open
Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
Collapse
|
29
|
Buerger M, Klein O, Kapahnke S, Mueller V, Frese JP, Omran S, Greiner A, Sommerfeld M, Kaschina E, Jannasch A, Dittfeld C, Mahlmann A, Hinterseher I. Use of MALDI Mass Spectrometry Imaging to Identify Proteomic Signatures in Aortic Aneurysms after Endovascular Repair. Biomedicines 2021; 9:biomedicines9091088. [PMID: 34572274 PMCID: PMC8465851 DOI: 10.3390/biomedicines9091088] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 08/15/2021] [Accepted: 08/24/2021] [Indexed: 11/16/2022] Open
Abstract
Endovascular repair (EVAR) has become the standard procedure in treating thoracic (TAA) or abdominal aortic aneurysms (AAA). Not entirely free of complications, a persisting perfusion of the aneurysm after EVAR, called Endoleak (EL), leads to reintervention and risk of secondary rupture. How the aortic wall responds to the implantation of a stentgraft and EL is mostly uncertain. We present a pilot study to identify peptide signatures and gain new insights in pathophysiological alterations of the aortic wall after EVAR using matrix-assisted laser desorption or ionization mass spectrometry imaging (MALDI-MSI). In course of or accompanying an open aortic repair, tissue sections from 15 patients (TAA = 5, AAA = 5, EVAR = 5) were collected. Regions of interest (tunica media and tunica adventitia) were defined and univariate (receiver operating characteristic analysis) statistical analysis for subgroup comparison was used. This proof-of-concept study demonstrates that MALDI-MSI is feasible to identify discriminatory peptide signatures separating TAA, AAA and EVAR. Decreased intensity distributions for actin, tropomyosin, and troponin after EVAR suggest impaired contractility in vascular smooth muscle cells. Furthermore, inability to provide energy caused by impaired respiratory chain function and continuous degradation of extracellular matrix components (collagen) might support aortic wall destabilization. In case of EL after EVAR, this mechanism may result in a weakened aortic wall with lacking ability to react on reinstating pulsatile blood flow.
Collapse
Affiliation(s)
- Matthias Buerger
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Oliver Klein
- BIH Center for Regenerative Therapies BCRT, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
| | - Sebastian Kapahnke
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Verena Mueller
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Jan Paul Frese
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Safwan Omran
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Andreas Greiner
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Manuela Sommerfeld
- Center for Cardiovascular Research (CCR), Institute of Pharmacology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hessische Str. 3-4, 10115 Berlin, Germany; (M.S.); (E.K.)
| | - Elena Kaschina
- Center for Cardiovascular Research (CCR), Institute of Pharmacology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hessische Str. 3-4, 10115 Berlin, Germany; (M.S.); (E.K.)
| | - Anett Jannasch
- Department of Cardiac Surgery, Herzzentrum Dresden, Medical Faculty Carl Gustav Carus Dresden, Technische Universität Dresden, 01307 Dresden, Germany; (A.J.); (C.D.)
| | - Claudia Dittfeld
- Department of Cardiac Surgery, Herzzentrum Dresden, Medical Faculty Carl Gustav Carus Dresden, Technische Universität Dresden, 01307 Dresden, Germany; (A.J.); (C.D.)
| | - Adrian Mahlmann
- University Center for Vascular Medicine, Department of Medicine—Section Angiology, University Hospital Carl Gustav Carus, Technische Universität, 01307 Dresden, Germany;
| | - Irene Hinterseher
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
- Medizinische Hochschule Brandenburg Theordor Fontane, 16816 Neuruppin, Germany
- Correspondence: ; Tel.: +49-30-450-522725
| |
Collapse
|
30
|
Noberini R, Savoia EO, Brandini S, Greco F, Marra F, Bertalot G, Pruneri G, McDonnell LA, Bonaldi T. Spatial epi-proteomics enabled by histone post-translational modification analysis from low-abundance clinical samples. Clin Epigenetics 2021; 13:145. [PMID: 34315505 PMCID: PMC8317427 DOI: 10.1186/s13148-021-01120-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/18/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Increasing evidence linking epigenetic mechanisms and different diseases, including cancer, has prompted in the last 15 years the investigation of histone post-translational modifications (PTMs) in clinical samples. Methods allowing the isolation of histones from patient samples followed by the accurate and comprehensive quantification of their PTMs by mass spectrometry (MS) have been developed. However, the applicability of these methods is limited by the requirement for substantial amounts of material. RESULTS To address this issue, in this study we streamlined the protein extraction procedure from low-amount clinical samples and tested and implemented different in-gel digestion strategies, obtaining a protocol that allows the MS-based analysis of the most common histone PTMs from laser microdissected tissue areas containing as low as 1000 cells, an amount approximately 500 times lower than what is required by available methods. We then applied this protocol to breast cancer patient laser microdissected tissues in two proof-of-concept experiments, identifying differences in histone marks in heterogeneous regions selected by either morphological evaluation or MALDI MS imaging. CONCLUSIONS These results demonstrate that analyzing histone PTMs from very small tissue areas and detecting differences from adjacent tumor regions is technically feasible. Our method opens the way for spatial epi-proteomics, namely the investigation of epigenetic features in the context of tissue and tumor heterogeneity, which will be instrumental for the identification of novel epigenetic biomarkers and aberrant epigenetic mechanisms.
Collapse
Affiliation(s)
- Roberta Noberini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| | - Evelyn Oliva Savoia
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Brandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesco Greco
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, 56127, Pisa, Italy
- Fondazione Pisana Per La Scienza ONLUS, 56107, San Giuliano Terme, PI, Italy
| | - Francesca Marra
- Department of Pathology, Fondazione IRCCS-Istituto Nazionale Tumori, Milan, Italy
| | - Giovanni Bertalot
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giancarlo Pruneri
- Department of Pathology, Fondazione IRCCS-Istituto Nazionale Tumori, Milan, Italy
| | - Liam A McDonnell
- Fondazione Pisana Per La Scienza ONLUS, 56107, San Giuliano Terme, PI, Italy
| | - Tiziana Bonaldi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| |
Collapse
|
31
|
Tian S, Hou Z, Zuo X, Xiong W, Huang G. Automatic Registration of the Mass Spectrometry Imaging Data of Sagittal Brain Slices to the Reference Atlas. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1789-1797. [PMID: 34096712 DOI: 10.1021/jasms.1c00137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The registration of the mass spectrometry imaging (MSI) data with mouse brain tissue slices from the atlases could perform automatic anatomical interpretation, and the comparison of MSI data in particular brain regions from different mice could be accelerated. However, the current registration of MSI data with mouse brain tissue slices is mainly focused on the coronal. Although the sagittal plane is able to provide more information about brain regions on a single histological slice than the coronal, it is difficult to directly register the complete sagittal brain slices of a mouse as a result of the more significant individualized differences and more positional shifts of brain regions. Herein, by adding the auxiliary line on the two brain regions of central canal (CC) and cerebral peduncle (CP), the registration accuracy of the MSI data with sagittal brain slices has been improved (∼2-5-folds for different brain regions). Moreover, the histological sections with different degrees deformation and different dyeing effects have been used to verify that this pipeline has a certain universality. Our method facilitates the rapid comparison of sagittal plane MSI data from different animals and accelerates the application in the discovery of disease markers.
Collapse
Affiliation(s)
- Shuangshuang Tian
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Zhuanghao Hou
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Xin Zuo
- School of Life Sciences, Neurodegenerative Disorder Research Center, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| | - Wei Xiong
- School of Life Sciences, Neurodegenerative Disorder Research Center, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangming Huang
- Department of Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei Anhui 230026, P. R. China
| |
Collapse
|
32
|
Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging. Cancers (Basel) 2021; 13:cancers13133184. [PMID: 34202325 PMCID: PMC8269054 DOI: 10.3390/cancers13133184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The childhood tumor, neuroblastoma, has a broad clinical presentation. Risk assessment at diagnosis is particularly difficult in molecularly heterogeneous high-risk cases. Here we investigate the potential of imaging mass spectrometry to directly detect intratumor heterogeneity on the protein level in tissue sections. We show that this approach can produce discriminatory peptide signatures separating high- from low- and intermediate-risk tumors, identify 8 proteins aassociated with these signatures and validate two marker proteins using tissue immunostaining that have promise for further basic and translational research in neuroblastoma. We provide proof-of-concept that mass spectrometry-based technology could assist early risk assessment in neuroblastoma and provide insights into peptide signature-based detection of intratumor heterogeneity. Abstract Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.
Collapse
|
33
|
Angel PM, Rujchanarong D, Pippin S, Spruill L, Drake R. Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge. Expert Rev Proteomics 2021; 18:423-436. [PMID: 34129411 PMCID: PMC8717608 DOI: 10.1080/14789450.2021.1941893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence, and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples. AREAS COVERED We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented. EXPERT OPINION The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.
Collapse
Affiliation(s)
- Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Denys Rujchanarong
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Sarah Pippin
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Laura Spruill
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Richard Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| |
Collapse
|
34
|
Davoli E, Zucchetti M, Matteo C, Ubezio P, D'Incalci M, Morosi L. THE SPACE DIMENSION AT THE MICRO LEVEL: MASS SPECTROMETRY IMAGING OF DRUGS IN TISSUES. MASS SPECTROMETRY REVIEWS 2021; 40:201-214. [PMID: 32501572 DOI: 10.1002/mas.21633] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/24/2020] [Accepted: 04/29/2020] [Indexed: 06/11/2023]
Abstract
Mass spectrometry imaging (MSI) has seen remarkable development in recent years. The possibility of getting quantitative or semiquantitative data, while maintaining the spatial component in the tissues has opened up unique study possibilities. Now with a spatial window of few tens of microns, we can characterize the events occurring in tissue subcompartments in physiological and pathological conditions. For example, in oncology-especially in preclinical models-we can quantitatively measure drug distribution within tumors, correlating it with pharmacological treatments intended to modify it. We can also study the local effects of the drug in the tissue, and their effects in relation to histology. This review focuses on the main results in the field of drug MSI in clinical pharmacology, looking at the literature on the distribution of drugs in human tissues, and also the first preclinical evidence of drug intratissue effects. The main instrumental techniques are discussed, looking at the different instrumentation, sample preparation protocols, and raw data management employed to obtain the sensitivity required for these studies. Finally, we review the applications that describe in situ metabolic events and pathways induced by the drug, in animal models, showing that MSI makes it possible to study effects that go beyond the simple concentration of the drug, maintaining the space dimension. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.
Collapse
Affiliation(s)
- Enrico Davoli
- Laboratory of Mass Spectrometry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Massimo Zucchetti
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Cristina Matteo
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Paolo Ubezio
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Maurizio D'Incalci
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Lavinia Morosi
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| |
Collapse
|
35
|
Greco F, Quercioli L, Pucci A, Rocchiccioli S, Ferrari M, Recchia FA, McDonnell LA. Mass Spectrometry Imaging as a Tool to Investigate Region Specific Lipid Alterations in Symptomatic Human Carotid Atherosclerotic Plaques. Metabolites 2021; 11:250. [PMID: 33919525 PMCID: PMC8073208 DOI: 10.3390/metabo11040250] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 12/01/2022] Open
Abstract
Atherosclerosis is characterized by fatty plaques in large and medium sized arteries. Their rupture can causes thrombi, occlusions of downstream vessels and adverse clinical events. The investigation of atherosclerotic plaques is made difficult by their highly heterogeneous nature. Here we propose a spatially resolved approach based on matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging to investigate lipids in specific regions of atherosclerotic plaques. The method was applied to a small dataset including symptomatic and asymptomatic human carotid atherosclerosis plaques. Tissue sections of symptomatic and asymptomatic human carotid atherosclerotic plaques were analyzed by MALDI mass spectrometry imaging (MALDI MSI) of lipids, and adjacent sections analyzed by histology and immunofluorescence. These multimodal datasets were used to compare the lipid profiles of specific histopathological regions within the plaque. The lipid profiles of macrophage-rich regions and intimal vascular smooth muscle cells exhibited the largest changes associated with plaque outcome. Macrophage-rich regions from symptomatic lesions were found to be enriched in sphingomyelins, and intimal vascular smooth muscle cells of symptomatic plaques were enriched in cholesterol and cholesteryl esters. The proposed method enabled the MALDI MSI analysis of specific regions of the atherosclerotic lesion, confirming MALDI MSI as a promising tool for the investigation of histologically heterogeneous atherosclerotic plaques.
Collapse
Affiliation(s)
- Francesco Greco
- Institute of Life Sciences, Sant’Anna School of Advanced Studies, 56127 Pisa, Italy; (F.G.); (F.A.R.)
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme (PI), Italy
| | - Laura Quercioli
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria Pisana, 56124 Pisa, Italy; (L.Q.); (M.F.)
| | - Angela Pucci
- Department of Histopathology, University Hospital, 56124 Pisa, Italy;
| | - Silvia Rocchiccioli
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy;
| | - Mauro Ferrari
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria Pisana, 56124 Pisa, Italy; (L.Q.); (M.F.)
| | - Fabio A. Recchia
- Institute of Life Sciences, Sant’Anna School of Advanced Studies, 56127 Pisa, Italy; (F.G.); (F.A.R.)
- Cardiovascular Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
| | - Liam A. McDonnell
- Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme (PI), Italy
| |
Collapse
|
36
|
Guo L, Hu Z, Zhao C, Xu X, Wang S, Xu J, Dong J, Cai Z. Data Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry Imaging. Anal Chem 2021; 93:4788-4793. [PMID: 33683863 DOI: 10.1021/acs.analchem.0c05242] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.
Collapse
Affiliation(s)
- Lei Guo
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zhenxing Hu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China.,Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Camperdown Sydney, NSW 2006, Australia
| | - Shujuan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing 102206, China
| | - Jingjing Xu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| |
Collapse
|
37
|
Hu H, Yin R, Brown HM, Laskin J. Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding. Anal Chem 2021; 93:3477-3485. [PMID: 33570915 PMCID: PMC7904669 DOI: 10.1021/acs.analchem.0c04798] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis were treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map was assembled from segment candidates that were generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections that were acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.
Collapse
Affiliation(s)
- Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ruichuan Yin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Hilary M Brown
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| |
Collapse
|
38
|
Murta T, Steven RT, Nikula CJ, Thomas SA, Zeiger LB, Dexter A, Elia EA, Yan B, Campbell AD, Goodwin RJA, Takáts Z, Sansom OJ, Bunch J. Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses. Anal Chem 2021; 93:2309-2316. [PMID: 33395266 DOI: 10.1021/acs.analchem.0c04179] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.
Collapse
Affiliation(s)
- Teresa Murta
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Rory T Steven
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Chelsea J Nikula
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Spencer A Thomas
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Lucas B Zeiger
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Alex Dexter
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Efstathios A Elia
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Bin Yan
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | | | - Richard J A Goodwin
- Imaging and AI, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, U.K
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K
| | - Zoltan Takáts
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Josephine Bunch
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
- The Rosalind Franklin Institute, Oxfordshire OX11 0FA, U.K
| |
Collapse
|
39
|
Mohamed SA, Taube ET, Thiele H, Noack F, Nebrich G, Mohamady K, Hanke T, Klein O. Evaluation of the Aortopathy in the Ascending Aorta: The Novelty of Using Matrix-Assisted Laser Desorption/Ionization Imaging. Proteomics Clin Appl 2021; 15:e2000047. [PMID: 33270371 DOI: 10.1002/prca.202000047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
PURPOSE Histopathological evaluation presents conflicting reports regarding aortic abnormalities. The authors aim to present proof-of-concept study to explore the feasibility of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) in combination with histopathology for characterizing alterations in the aneurysmal ascending formalin-fixed paraffin-embedded (FFPE) aorta tissue. EXPERIMENTAL DESIGN The authors assess FFPE specimens from patients with a dilated aorta and bicuspid aortic valve (BAV), those with a standard tricuspid aortic valve (TAV), and those with Marfan syndrome (MFS) via histopathology and grade the conditions for elastic fiber fragmentation (EFF) and MALDI-IMS. The proteins using liquid chromatographic-mass spectrometry are identified and the results are confirmed by immunohistochemistry. RESULTS There is significant difference in terms of EFF between MFS and BAV, and TAV and BAV. Characteristic peptide signatures and m/z values in the EFF facilitate the characterization among the aortic specimens of BAV, MFS, and TAV. The m/z values from the aortic alpha smooth muscle actin and myosin heavy chains significantly increase in BAV compared with MFS and TAV. These findings are confirmed by immunohistochemistry. CONCLUSION The results represent a strategy that uses MALDI-IMS in combination with histopathology as promising approaches to characterize spatial alteration in the structure of the aneurysmal ascending aorta.
Collapse
Affiliation(s)
- Salah A Mohamed
- Department of Cardiac and Thoracic Vascular Surgery, UKSH-Campus Luebeck, Luebeck, 23538, Germany
| | - Eliane T Taube
- Charité-Universitaetsmedizin, Institute for Pathology, Berlin, 10117, Germany
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, Luebeck, 23538, Germany
| | - Frank Noack
- Institute of Pathology Martin-Luther Hospital, Berlin, 14193, Germany
| | - Grit Nebrich
- Berlin Institute of Health Center for Regenerative Therapies & Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Campus Virchow Klinikum (CVK), Charité - Universitätsmedizin Berlin, Berlin, 13353, Germany
| | | | | | - Oliver Klein
- Berlin Institute of Health Center for Regenerative Therapies & Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Campus Virchow Klinikum (CVK), Charité - Universitätsmedizin Berlin, Berlin, 13353, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, 13353, Germany
| |
Collapse
|
40
|
Hadjiiski L, Samala R, Chan HP. Image Processing Analytics: Enhancements and Segmentation. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
|
41
|
Pappritz K, Klein O, Dong F, Hamdani N, Kovacs A, O'Flynn L, Elliman S, O'Brien T, Tschöpe C, Van Linthout S. MALDI-IMS as a Tool to Determine the Myocardial Response to Syndecan-2-Selected Mesenchymal Stromal Cell Application in an Experimental Model of Diabetic Cardiomyopathy. Proteomics Clin Appl 2021; 15:e2000050. [PMID: 33068073 DOI: 10.1002/prca.202000050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/12/2020] [Indexed: 01/08/2023]
Abstract
PURPOSE Mesenchymal stromal cells (MSC) are an attractive tool for treatment of diabetic cardiomyopathy. Syndecan-2/CD362 has been identified as a functional marker for MSC isolation. Imaging mass spectrometry (IMS) allows for the characterization of therapeutic responses in the left ventricle. This study aims to investigate whether IMS can assess the therapeutic effect of CD362+ -selected MSC on early onset experimental diabetic cardiomyopathy. EXPERIMENTAL DESIGN 1 × 106 wild type (WT), CD362- , or CD362+ MSC are intravenously injected into db/db mice. Four weeks later, mice are hemodynamically characterized and subsequently sacrificed for IMS combined with bottom-up mass spectrometry, and isoform and phosphorylation analyses of cardiac titin. RESULTS Overall alterations of the cardiac proteome signatures, especially titin, are observed in db/db compared to control mice. Interestingly, only CD362+ MSC can overcome the reduced titin intensity distribution and shifts the isoform ratio toward the more compliant N2BA form. In contrast, WT and CD362- MSCs improve all-titin phosphorylation and protein kinase G activity, which is reflected in an improvement in diastolic performance. CONCLUSIONS AND CLINICAL RELEVANCE IMS enables the characterization of differences in titin intensity distribution following MSC application. However, further analysis of titin phosphorylation is needed to allow for the assessment of the therapeutic efficacy of MSC.
Collapse
Affiliation(s)
- Kathleen Pappritz
- Berlin-Brandenburg Center for Regenerative Therapies and Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum (CVK), Berlin, 13353 and 10178, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, 13347, Germany
| | - Oliver Klein
- Berlin-Brandenburg Center for Regenerative Therapies and Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum (CVK), Berlin, 13353 and 10178, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, 13347, Germany
| | - Fengquan Dong
- Berlin-Brandenburg Center for Regenerative Therapies and Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum (CVK), Berlin, 13353 and 10178, Germany
| | - Nazha Hamdani
- Department of Physiology, Institute of Physiology, Ruhr University Bochum, Bochum, 44780, Germany
| | - Arpad Kovacs
- Department of Physiology, Institute of Physiology, Ruhr University Bochum, Bochum, 44780, Germany
| | - Lisa O'Flynn
- Orbsen Therapeutics, National University of Ireland (NUIG), Galway, H91 TK33, Ireland
| | - Steve Elliman
- Orbsen Therapeutics, National University of Ireland (NUIG), Galway, H91 TK33, Ireland
| | - Timothy O'Brien
- Regenerative Medicine Institute and Department of Medicine, NUIG, Galway, H91 TK33, Ireland
| | - Carsten Tschöpe
- Berlin-Brandenburg Center for Regenerative Therapies and Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum (CVK), Berlin, 13353 and 10178, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, 13347, Germany
- Department of Cardiology, Charité - Universitätsmedizin Berlin, CVK, Berlin, 13353, Germany
| | - Sophie Van Linthout
- Berlin-Brandenburg Center for Regenerative Therapies and Berlin Institute of Health Center for Regenerative Therapies (BCRT), Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum (CVK), Berlin, 13353 and 10178, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, 13347, Germany
| |
Collapse
|
42
|
Ovchinnikova K, Stuart L, Rakhlin A, Nikolenko S, Alexandrov T. ColocML: machine learning quantifies co-localization between mass spectrometry images. Bioinformatics 2020; 36:3215-3224. [PMID: 32049317 PMCID: PMC7214035 DOI: 10.1093/bioinformatics/btaa085] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 01/22/2020] [Accepted: 02/04/2020] [Indexed: 12/24/2022] Open
Abstract
Motivation Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. Results We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency–inverse document frequency and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings, respectively). We illustrate these measures by inferring co-localization properties of 10 273 molecules from 3685 public METASPACE datasets. Availability and implementation https://github.com/metaspace2020/coloc. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Katja Ovchinnikova
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lachlan Stuart
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Sergey Nikolenko
- National Research Institute Higher School of Economics.,Steklov Institute of Mathematics at St. Petersburg, St. Petersburg, Russia
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.,Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|
43
|
Klein O, Haeckel A, Reimer U, Nebrich G, Schellenberger E. Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions. Sci Rep 2020; 10:15522. [PMID: 32968143 PMCID: PMC7511933 DOI: 10.1038/s41598-020-72436-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/14/2020] [Indexed: 01/05/2023] Open
Abstract
Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.
Collapse
Affiliation(s)
- Oliver Klein
- Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Akvile Haeckel
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Ulf Reimer
- JPT Peptide Technologies GmbH, Volmerstraße 5, 12489, Berlin, Germany
| | - Grit Nebrich
- Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Eyk Schellenberger
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
| |
Collapse
|
44
|
Lieb F, Boskamp T, Stark HG. Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers. J Proteomics 2020; 225:103852. [DOI: 10.1016/j.jprot.2020.103852] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/23/2022]
|
45
|
Goodwin RJA, Takats Z, Bunch J. A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery. SLAS DISCOVERY 2020; 25:963-976. [PMID: 32713279 DOI: 10.1177/2472555220941843] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.
Collapse
Affiliation(s)
- Richard J A Goodwin
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.,Institute of Infection, Immunity, and Inflammation, College of Medical, Veterinary, and Life Sciences, University of Glasgow, UK
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK
| | - Josephine Bunch
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK.,National Physical Laboratory, Teddington, London, UK
| |
Collapse
|
46
|
Kulbe H, Klein O, Wu Z, Taube ET, Kassuhn W, Horst D, Darb-Esfahani S, Jank P, Abobaker S, Ringel F, du Bois A, Heitz F, Sehouli J, Braicu EI. Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging. Cancers (Basel) 2020; 12:cancers12082000. [PMID: 32707805 PMCID: PMC7463791 DOI: 10.3390/cancers12082000] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/19/2020] [Accepted: 07/20/2020] [Indexed: 12/31/2022] Open
Abstract
With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC > 0.6 or < 0.4; p < 0.01), and 13 of them could be annotated to proteins. Strongest expression levels of specific peptides linked to Keratin type1 and Collagen alpha-2(I) were observed and associated with poor prognosis (AUC > 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC.
Collapse
Affiliation(s)
- Hagen Kulbe
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies BCRT, Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany; (O.K.); (Z.W.)
| | - Zhiyang Wu
- BIH Center for Regenerative Therapies BCRT, Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany; (O.K.); (Z.W.)
| | - Eliane T. Taube
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (E.T.T.); (D.H.); (P.J.)
| | - Wanja Kassuhn
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (E.T.T.); (D.H.); (P.J.)
| | | | - Paul Jank
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (E.T.T.); (D.H.); (P.J.)
- Institute of Pathology, Philipps-University Marburg, 35032 Marburg, Germany
| | - Salem Abobaker
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Frauke Ringel
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Andreas du Bois
- Evangelische Kliniken Essen-Mitte Klinik für Gynäkologie und gynäkologische Onkologie, 45136 Essen, Germany (F.H.)
| | - Florian Heitz
- Evangelische Kliniken Essen-Mitte Klinik für Gynäkologie und gynäkologische Onkologie, 45136 Essen, Germany (F.H.)
| | - Jalid Sehouli
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Elena I. Braicu
- Tumorbank Ovarian Cancer Network, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (H.K.); (W.K.); (S.A.); (F.R.); (J.S.)
- Department of Gynecology, European Competence Center for Ovarian Cancer, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
- Correspondence: ; Tel.: +49-(0)30-450-664469
| |
Collapse
|
47
|
Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
Collapse
Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| |
Collapse
|
48
|
Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
Collapse
Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
| |
Collapse
|
49
|
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. Int J Mol Sci 2020; 21:ijms21082873. [PMID: 32326049 PMCID: PMC7216093 DOI: 10.3390/ijms21082873] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/16/2020] [Accepted: 04/18/2020] [Indexed: 01/15/2023] Open
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
Collapse
|
50
|
Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020; 3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
Collapse
Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
| |
Collapse
|